diff --git a/docs/serving/parallelism_scaling.md b/docs/serving/parallelism_scaling.md
index fa7fc1b290d50..cef1127fc5c15 100644
--- a/docs/serving/parallelism_scaling.md
+++ b/docs/serving/parallelism_scaling.md
@@ -66,7 +66,7 @@ Ray is a distributed computing framework for scaling Python programs. Multi-node
 
 vLLM uses Ray to manage the distributed execution of tasks across multiple nodes and control where execution happens.
 
-Ray also offers high-level APIs for large-scale [offline batch inference](https://docs.ray.io/en/latest/data/working-with-llms.html) and [online serving](https://docs.ray.io/en/latest/serve/llm/serving-llms.html) that can leverage vLLM as the engine. These APIs add production-grade fault tolerance, scaling, and distributed observability to vLLM workloads.
+Ray also offers high-level APIs for large-scale [offline batch inference](https://docs.ray.io/en/latest/data/working-with-llms.html) and [online serving](https://docs.ray.io/en/latest/serve/llm) that can leverage vLLM as the engine. These APIs add production-grade fault tolerance, scaling, and distributed observability to vLLM workloads.
 
 For details, see the [Ray documentation](https://docs.ray.io/en/latest/index.html).
 
@@ -104,7 +104,7 @@ Note that `VLLM_HOST_IP` is unique for each worker. Keep the shells running thes
 From any node, enter a container and run `ray status` and `ray list nodes` to verify that Ray finds the expected number of nodes and GPUs.
 
 !!! tip
-    Alternatively, set up the Ray cluster using KubeRay. For more information, see [KubeRay vLLM documentation](https://docs.ray.io/en/latest/cluster/kubernetes/examples/vllm-rayservice.html).
+    Alternatively, set up the Ray cluster using KubeRay. For more information, see [KubeRay vLLM documentation](https://docs.ray.io/en/latest/cluster/kubernetes/examples/rayserve-llm-example.html).
 
 ### Running vLLM on a Ray cluster